
"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		SwinConfig(image_size=1_92					)

       if "base" in model_name:
              UpperCamelCase       			=		6
              UpperCamelCase       			=		1_28
              UpperCamelCase       			=		(2, 2, 18, 2)
              UpperCamelCase       			=		(4, 8, 16, 32)
       elif "large" in model_name:
              UpperCamelCase       			=		12
              UpperCamelCase       			=		1_92
              UpperCamelCase       			=		(2, 2, 18, 2)
              UpperCamelCase       			=		(6, 12, 24, 48)
       else:
              raise ValueError("Model not supported, only supports base and large variants"					)

       UpperCamelCase       			=		window_size
       UpperCamelCase       			=		embed_dim
       UpperCamelCase       			=		depths
       UpperCamelCase       			=		num_heads

       return config



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       if "encoder.mask_token" in name:
              UpperCamelCase       			=		name.replace("encoder.mask_token"		,   "embeddings.mask_token"					)
       if "encoder.patch_embed.proj" in name:
              UpperCamelCase       			=		name.replace("encoder.patch_embed.proj"		,   "embeddings.patch_embeddings.projection"					)
       if "encoder.patch_embed.norm" in name:
              UpperCamelCase       			=		name.replace("encoder.patch_embed.norm"		,   "embeddings.norm"					)
       if "attn.proj" in name:
              UpperCamelCase       			=		name.replace("attn.proj"		,   "attention.output.dense"					)
       if "attn" in name:
              UpperCamelCase       			=		name.replace("attn"		,   "attention.self"					)
       if "norm1" in name:
              UpperCamelCase       			=		name.replace("norm1"		,   "layernorm_before"					)
       if "norm2" in name:
              UpperCamelCase       			=		name.replace("norm2"		,   "layernorm_after"					)
       if "mlp.fc1" in name:
              UpperCamelCase       			=		name.replace("mlp.fc1"		,   "intermediate.dense"					)
       if "mlp.fc2" in name:
              UpperCamelCase       			=		name.replace("mlp.fc2"		,   "output.dense"					)

       if name == "encoder.norm.weight":
              UpperCamelCase       			=		"layernorm.weight"
       if name == "encoder.norm.bias":
              UpperCamelCase       			=		"layernorm.bias"

       if "decoder" in name:
              pass
       else:
              UpperCamelCase       			=		"swin." + name

       return name



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       for key in orig_state_dict.copy().keys():
              UpperCamelCase       			=		orig_state_dict.pop(UpperCAmelCase_					)

              if "attn_mask" in key:
                     pass
              elif "qkv" in key:
                     UpperCamelCase       			=		key.split("."					)
                     UpperCamelCase       			=		int(key_split[2]					)
                     UpperCamelCase       			=		int(key_split[4]					)
                     UpperCamelCase       			=		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                     if "weight" in key:
                            UpperCamelCase       			=		val[:dim, :]
                            UpperCamelCase       			=		val[
                                dim : dim * 2, :
                            ]
                            UpperCamelCase       			=		val[-dim:, :]
                     else:
                            UpperCamelCase       			=		val[
                                :dim
                            ]
                            UpperCamelCase       			=		val[
                                dim : dim * 2
                            ]
                            UpperCamelCase       			=		val[
                                -dim:
                            ]
              else:
                     UpperCamelCase       			=		val

       return orig_state_dict



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       UpperCamelCase       			=		torch.load(UpperCAmelCase_		,   map_location="cpu"					)["model"]

       UpperCamelCase       			=		get_swin_config(UpperCAmelCase_					)
       UpperCamelCase       			=		SwinForMaskedImageModeling(UpperCAmelCase_					)
       model.eval()

       UpperCamelCase       			=		convert_state_dict(UpperCAmelCase_		,   UpperCAmelCase_					)
       model.load_state_dict(UpperCAmelCase_					)

       UpperCamelCase       			=		"http://images.cocodataset.org/val2017/000000039769.jpg"

       UpperCamelCase       			=		ViTImageProcessor(size={"height": 1_92, "width": 1_92}					)
       UpperCamelCase       			=		Image.open(requests.get(UpperCAmelCase_		,   stream=UpperCAmelCase_					).raw					)
       UpperCamelCase       			=		image_processor(images=UpperCAmelCase_		,   return_tensors="pt"					)

       with torch.no_grad():
              UpperCamelCase       			=		model(**UpperCAmelCase_					).logits

       print(outputs.keys()					)
       print("Looks ok!"					)

       if pytorch_dump_folder_path is not None:
              print(F"Saving model {model_name} to {pytorch_dump_folder_path}"					)
              model.save_pretrained(UpperCAmelCase_					)

              print(F"Saving image processor to {pytorch_dump_folder_path}"					)
              image_processor.save_pretrained(UpperCAmelCase_					)

       if push_to_hub:
              print(F"Pushing model and image processor for {model_name} to hub"					)
              model.push_to_hub(F"microsoft/{model_name}"					)
              image_processor.push_to_hub(F"microsoft/{model_name}"					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""swin-base-simmim-window6-192""",
          type=str,
          choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
          help="""Name of the Swin SimMIM model you'd like to convert.""",
      )
      parser.add_argument(
          """--checkpoint_path""",
          default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
          type=str,
          help="""Path to the original PyTorch checkpoint (.pth file).""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      SCREAMING_SNAKE_CASE			  =  parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel



class 	__a	:






       def __init__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				str=13							,   UpperCAmelCase_			:				Optional[int]=7							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				Optional[Any]=True							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				int=True							,   UpperCAmelCase_			:				Dict=99							,   UpperCAmelCase_			:				str=32							,   UpperCAmelCase_			:				List[Any]=5							,   UpperCAmelCase_			:				str=4							,   UpperCAmelCase_			:				Union[str, Any]=4							,   UpperCAmelCase_			:				Optional[int]="gelu"							,   UpperCAmelCase_			:				Tuple=0.0							,   UpperCAmelCase_			:				Any=0.1							,   UpperCAmelCase_			:				int=True							,   UpperCAmelCase_			:				Union[str, Any]=512							,   UpperCAmelCase_			:				int=16							,   UpperCAmelCase_			:				Optional[Any]=2							,   UpperCAmelCase_			:				str=0.02							,   UpperCAmelCase_			:				Any=3							,   UpperCAmelCase_			:				Any=4							,   UpperCAmelCase_			:				Optional[Any]=None							,   )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		seq_length
              UpperCamelCase       			=		is_training
              UpperCamelCase       			=		use_input_mask
              UpperCamelCase       			=		use_token_type_ids
              UpperCamelCase       			=		use_labels
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_multiple_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout
              UpperCamelCase       			=		attention_dropout
              UpperCamelCase       			=		weight_tying
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		type_sequence_label_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		num_labels
              UpperCamelCase       			=		num_choices
              UpperCamelCase       			=		scope






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size      )

              UpperCamelCase       			=		None
              if self.use_input_mask:
                     UpperCamelCase       			=		random_attention_mask([self.batch_size, self.seq_length]      )

              UpperCamelCase       			=		None
              if self.use_labels:
                     UpperCamelCase       			=		ids_tensor([self.batch_size, self.seq_length]							,   self.num_labels      )

              UpperCamelCase       			=		self.get_config()

              return config, input_ids, input_mask, token_labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Dict:







              """simple docstring"""
              return GPTNeoXJapaneseConfig(
                  vocab_size=self.vocab_size							,   hidden_size=self.hidden_size							,   num_hidden_layers=self.num_hidden_layers							,   num_attention_heads=self.num_attention_heads							,   intermediate_multiple_size=self.intermediate_multiple_size							,   hidden_act=self.hidden_act							,   hidden_dropout=self.hidden_dropout							,   attention_dropout=self.attention_dropout							,   weight_tying=self.weight_tying							,   max_position_embeddings=self.max_position_embeddings							,   type_vocab_size=self.type_vocab_size							,   is_decoder=UpperCAmelCase_							,   initializer_range=self.initializer_range							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.prepare_config_and_inputs()

              UpperCamelCase       			=		True

              return config, input_ids, input_mask, token_labels






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseModel(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )
              UpperCamelCase       			=		model(UpperCAmelCase_      )
              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				List[str]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		True
              UpperCamelCase       			=		GPTNeoXJapaneseModel(UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_      )
              self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				Dict							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   labels=UpperCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.vocab_size)      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		True
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM(config=UpperCAmelCase_      )
              model.to(UpperCAmelCase_      )
              model.eval()

              # first forward pass
              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   use_cache=UpperCAmelCase_      )
              UpperCamelCase       			=		outputs.past_key_values

              # create hypothetical multiple next token and extent to next_input_ids
              UpperCamelCase       			=		ids_tensor((self.batch_size, 3)							,   config.vocab_size      )
              UpperCamelCase       			=		ids_tensor((self.batch_size, 3)							,   vocab_size=2      )

              # append to next input_ids and
              UpperCamelCase       			=		torch.cat([input_ids, next_tokens]							,   dim=-1      )
              UpperCamelCase       			=		torch.cat([input_mask, next_mask]							,   dim=-1      )

              UpperCamelCase       			=		model(UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_      )
              UpperCamelCase       			=		output_from_no_past["hidden_states"][0]
              UpperCamelCase       			=		model(
                  UpperCAmelCase_							,   attention_mask=UpperCAmelCase_							,   past_key_values=UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_							,   )["hidden_states"][0]

              # select random slice
              UpperCamelCase       			=		ids_tensor((1,)							,   output_from_past.shape[-1]      ).item()
              UpperCamelCase       			=		output_from_no_past[:, -3:, random_slice_idx].detach()
              UpperCamelCase       			=		output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		self.prepare_config_and_inputs()
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		config_and_inputs
              UpperCamelCase       			=		{"input_ids": input_ids, "attention_mask": input_mask}
              return config, inputs_dict





@require_torch
class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Union[str, Any]				=      (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       UpperCamelCase_		:       Optional[int]				=      (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       UpperCamelCase_		:       List[str]				=      (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       UpperCamelCase_		:       Optional[Any]				=      False
       UpperCamelCase_		:       Tuple				=      False
       UpperCamelCase_		:       Optional[int]				=      False
       UpperCamelCase_		:       List[Any]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		GPTNeoXJapaneseModelTester(self      )
              UpperCamelCase       			=		ConfigTester(self							,   config_class=UpperCAmelCase_							,   hidden_size=37      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	Any:







              """simple docstring"""
              self.config_tester.run_common_tests()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Dict:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	List[Any]:







              """simple docstring"""
              # This regression test was failing with PyTorch < 1.3
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs_for_decoder()

              UpperCamelCase       			=		None

              self.model_tester.create_and_check_model_as_decoder(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase_      )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		"abeja/gpt-neox-japanese-2.7b"

              UpperCamelCase       			=		["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

              UpperCamelCase       			=		[
                  "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
                  "100年後に必要とされる会社は、「人」が中心の会社です。",
                  "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
                  "国境の長いトンネルを抜けると、そこは雪国だった。",
                  "美味しい日本食といえば、やっぱりお寿司ですよね。",
              ]

              UpperCamelCase       			=		GPTNeoXJapaneseTokenizer.from_pretrained(UpperCAmelCase_      )
              UpperCamelCase       			=		GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCAmelCase_      )

              UpperCamelCase       			=		[]
              for prompt in prompts:
                     UpperCamelCase       			=		tokenizer(UpperCAmelCase_							,   return_tensors="pt"      ).input_ids
                     UpperCamelCase       			=		model.generate(UpperCAmelCase_							,   max_length=50      )
                     UpperCamelCase       			=		tokenizer.batch_decode(UpperCAmelCase_							,   skip_special_tokens=UpperCAmelCase_      )
                     predicted_outputs += generated_string
              self.assertListEqual(UpperCAmelCase_							,   UpperCAmelCase_      )





